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LLM has been struggling to solve search and optimization at scale when feedback is stochastic. We propose a simple solution, POLCA, using text embedding with “provable” guarantee. Excited to see the first theoretically correct work of LLM optimization. Kudos to @XuanfeiRen
🚀 How can we make LLM-based optimization stable and scalable when the feedback signal is stochastic?
Introducing POLCA: a framework for robust, scalable stochastic generative optimization.
Paper: arxiv.org/abs/2603.14769
Code: github.com/rlx-lab/POLCA
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Presenting two posters with @allenainie @XuanfeiRen today at #ICML2026
1. “Formalizing Learning from Language Feedback with Provable Guarantees” 10:30am, Hall A 4500
2. “POLCA: Stochastic Generative Optimization with LLM”, 5pm, Hall A 1810
Come check them out!
1. “Formalizing Learning from Language Feedback with Provable Guarantees” 10:30am, Hall A 4500
2. “POLCA: Stochastic Generative Optimization with LLM”, 5pm, Hall A 1810
Come check them out!
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Just arrived at #ICML2026 Looking forward to meeting everyone once I get passed this long long line of registration …
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Looking for Google research student researcher (PhD student) to work on LLM and agent related learning.
Preferred background: RL/game theory, agentic system, LLM training.
Candidate will work closely with me and @allenainie
Email me if you are interested. 😀
Preferred background: RL/game theory, agentic system, LLM training.
Candidate will work closely with me and @allenainie
Email me if you are interested. 😀
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Ching-An Cheng retweeted
I have served as AC for NeurIPS every year since 2020. Just declined (with messages adapted from @xuanalogue). At least the organizers owe the community an explanation why they are the only major ML venue adopting such a policy.
I have declined volunteer review for @NeurIPSConf in light of their policy on sanctioned institutions. If you feel similarly opposed, feel free to adapt my message.
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Ching-An Cheng retweeted
Too close to home?
Junior researcher: I’m publishing papers at NeurIPS, my students are happy, but my chair says I’m “not impactful enough.” I don’t know what that means.
Senior researcher: What did you tell them you accomplished last year?
Junior: 3 top-tier papers, a new theoretical result on regret bounds, and an invited talk.
Senior: And what did they hear?
Junior: That I published 3 papers?
Senior: They heard “I added to the publication count, but didn’t bring in grants or visibility for the department.”
Junior: But regret bounds are impactful!
Senior: To who?
Junior: To… theorists?
Senior: Your chair spends 20 minutes a month justifying your position to the dean. Can they use regret bounds to argue for funding?
Junior: …probably not.
Senior: What external metrics did your work move?
Junior: One collaboration, one best paper award, and some citations. We don’t really track grant impact.
Senior: There’s the problem. Half your contributions are invisible by design.
Junior: But theory is necessary. The field would break without it.
Senior: I believe you. The dean doesn’t care.
Junior: That seems unfair.
Senior: It is unfair. It’s also how academia works. Chairs get grilled on grants, rankings, and prestige, not the long-run stability of ML theory.
Junior: So what should I do?
Senior: Reframe. “Secured $500K in funding to explore foundational algorithms” sounds better than “proved a tighter regret bound.”
Junior: But I don’t have that funding.
Senior: Then you’re fighting academic reality without weapons.
Junior: I don’t have time to write grants and still publish.
Senior: Most junior faculty don’t. That’s the trap — you get judged on impact but don’t get impact resources.
Junior: So what do I do?
Senior: Acknowledge the game is rigged, then play it anyway.
Junior: Meaning?
Senior: Build collaborations that attract funding. Tie your theory to hot applied areas. Translate your results into language deans understand.
Junior: That feels political.
Senior: Everything above a certain level is political. The choice isn’t political vs pure. It’s visible vs irrelevant.
Junior: What if my chair still doesn’t care?
Senior: Then you’ve learned your chair doesn’t know how to evaluate theory. That’s a different problem — one you solve by finding a better environment.
Junior: This is harder than just proving good theorems.
Senior: Proving good theorems is table stakes. Surviving academia while proving good theorems — that’s the actual job.
Junior researcher: I’m publishing papers at NeurIPS, my students are happy, but my chair says I’m “not impactful enough.” I don’t know what that means.
Senior researcher: What did you tell them you accomplished last year?
Junior: 3 top-tier papers, a new theoretical result on regret bounds, and an invited talk.
Senior: And what did they hear?
Junior: That I published 3 papers?
Senior: They heard “I added to the publication count, but didn’t bring in grants or visibility for the department.”
Junior: But regret bounds are impactful!
Senior: To who?
Junior: To… theorists?
Senior: Your chair spends 20 minutes a month justifying your position to the dean. Can they use regret bounds to argue for funding?
Junior: …probably not.
Senior: What external metrics did your work move?
Junior: One collaboration, one best paper award, and some citations. We don’t really track grant impact.
Senior: There’s the problem. Half your contributions are invisible by design.
Junior: But theory is necessary. The field would break without it.
Senior: I believe you. The dean doesn’t care.
Junior: That seems unfair.
Senior: It is unfair. It’s also how academia works. Chairs get grilled on grants, rankings, and prestige, not the long-run stability of ML theory.
Junior: So what should I do?
Senior: Reframe. “Secured $500K in funding to explore foundational algorithms” sounds better than “proved a tighter regret bound.”
Junior: But I don’t have that funding.
Senior: Then you’re fighting academic reality without weapons.
Junior: I don’t have time to write grants and still publish.
Senior: Most junior faculty don’t. That’s the trap — you get judged on impact but don’t get impact resources.
Junior: So what do I do?
Senior: Acknowledge the game is rigged, then play it anyway.
Junior: Meaning?
Senior: Build collaborations that attract funding. Tie your theory to hot applied areas. Translate your results into language deans understand.
Junior: That feels political.
Senior: Everything above a certain level is political. The choice isn’t political vs pure. It’s visible vs irrelevant.
Junior: What if my chair still doesn’t care?
Senior: Then you’ve learned your chair doesn’t know how to evaluate theory. That’s a different problem — one you solve by finding a better environment.
Junior: This is harder than just proving good theorems.
Senior: Proving good theorems is table stakes. Surviving academia while proving good theorems — that’s the actual job.
Junior PM: I'm shipping everything on time, team loves me, but my manager says I'm "not strategic enough." I'm exhausted trying to figure out what that means.
Senior PM: What did you tell him you accomplished last quarter?
Junior PM: Delivered 5 features, reduced tech debt, improved team velocity by 15%.
Senior PM: And what did he hear?
Junior PM: That I delivered 5 features?
Senior PM: He heard "I kept the team busy with stuff that doesn't move numbers I get asked about."
Junior PM: But velocity improvement is strategic.
Senior PM: To who?
Junior PM: To... the team?
Senior PM: Your manager spends 20 minutes a week with his director explaining why you exist. Can he use velocity to justify your headcount?
Junior PM: I... probably not.
Senior PM: What business metrics did those 5 features move?
Junior PM: Three were tech debt, one was a sales request, one was compliance. We don't really measure impact on that stuff.
Senior PM: There's your problem. Half your work is invisible by design.
Junior PM: But that work was necessary. The platform would break without it.
Senior PM: I believe you. Your manager's director doesn't care.
Junior PM: That seems unfair.
Senior PM: It is unfair. It's also how companies work. Your manager gets grilled about revenue and retention, not platform stability.
Junior PM: So I should have said no to the tech debt?
Senior PM: You probably couldn't. But you should have framed it differently.
Junior PM: How?
Senior PM: "Prevented $200K in potential downtime costs" sounds better than "reduced tech debt."
Junior PM: But I don't have that number.
Senior PM: Then you're fighting organizational reality without weapons.
Junior PM: I don't have analytics support or time to instrument everything.
Senior PM: Most junior PMs don't. That's the trap - you get judged on business impact but don't get business resources.
Junior PM: So what do I do?
Senior PM: Acknowledge the game is rigged, then play it anyway.
Junior PM: Meaning?
Senior PM: Make allies in sales and marketing. They have the numbers you need. Shadow customer calls. Connect your work to their goals.
Junior PM: That feels political.
Senior PM: Everything above a certain level is political. The choice isn't political vs pure. It's visible vs irrelevant.
Junior PM: What if I try this and my manager still doesn't care?
Senior PM: Then you learn your manager doesn't know how to evaluate PM work. That's a different problem - one you solve by finding a better manager.
Junior PM: This is harder than just building good products.
Senior PM: Building good products is table stakes. Surviving organizational dysfunction while building good products - that's the actual job.
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Happening now at #RLC2025. Join us if you’re interested in program, agents and RL.
Kicking off #RLC2025 with our Workshop on Programmatic Reinforcement Learning! This workshop explores how programmatic representations can improve interpretability, generalization, efficiency, and safety in RL.
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Ching-An Cheng retweeted
🔄 We were nominated for Oral+top 1 in the MATH-AI workshp at #ICML!
🚨Why? ≈46 % of GitHub commits are AI-generated—but can we verify them correct?
📢 VeriBench challenges agents; turn Python into Lean code!
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📃 Paper: openreview.net/forum?id=rWkGF…
🚨Why? ≈46 % of GitHub commits are AI-generated—but can we verify them correct?
📢 VeriBench challenges agents; turn Python into Lean code!
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📃 Paper: openreview.net/forum?id=rWkGF…
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We are organizing a workshop tomorrow at #icml25. Come join us and checkout the latest on programmatic representation and agent learning
Our #ICML2025 Programmatic Representations for Agent Learning workshop will take place tomorrow, July 18th, at the West Meeting Room 301-305, exploring how programmatic representations can make agent learning more interpretable, generalizable, efficient, and safe! Come join us!
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Ching-An Cheng retweeted
Our #ICML2025 Programmatic Representations for Agent Learning workshop will take place tomorrow, July 18th, at the West Meeting Room 301-305, exploring how programmatic representations can make agent learning more interpretable, generalizable, efficient, and safe! Come join us!
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Starting my #ICML2025. Will be here until Saturday. Looking forward to meeting everyone 😀
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Ching-An Cheng retweeted
Provably Learning from Language Feedback
TLDR: RL theory can help us do better inference-time exploration with feedback.
Work done with @wanqiao_xu, @ruijie_zheng12, @chinganc_rl, @adityamodi94, @adith387
📰 arxiv.org/pdf/2506.10341
📍EXAIT Best Paper/Oral Sat 8:45-9:30 am
TLDR: RL theory can help us do better inference-time exploration with feedback.
Work done with @wanqiao_xu, @ruijie_zheng12, @chinganc_rl, @adityamodi94, @adith387
📰 arxiv.org/pdf/2506.10341
📍EXAIT Best Paper/Oral Sat 8:45-9:30 am
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Super excited about this work done by our former intern @wanqiao_xu . We show Learning from Language Feedback (LLF) with LLM can be formally studied with provable no-regret learning algorithms. This result builds a foundation toward new theories for LLM learning and optimization.
Decision-making with LLM can be studied with RL! Can an agent solve a task with text feedback (OS terminal, compiler, a person) efficiently? How can we understand the difficulty? We propose a new notion of learning complexity to study learning with language feedback only. 🧵👇
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Ching-An Cheng retweeted
Decision-making with LLM can be studied with RL! Can an agent solve a task with text feedback (OS terminal, compiler, a person) efficiently? How can we understand the difficulty? We propose a new notion of learning complexity to study learning with language feedback only. 🧵👇
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Check out this new optimization framework (github.com/datarobot/syftr) by #DataRobot that can automatically search for "Pareto-optimal" solutions for agentic workflows. It's built on our LLM generative optimization framework #Trace. Excited to see more applications of #Trace! 😎
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Ching-An Cheng retweeted
Our ICML & RLC workshops welcome contributions using programmatic representations as policies, reward functions, skill libraries, task generators, environment models, etc., to improve interpretability, generalization, efficiency, & safety in agent learning & RL! Please retweet 🙏
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Workshop on Programmatic Reinforcement Learning (RLC 2025)
- Web page: prl-workshop.github.io
- Submission Deadline: May 30, 2025, AoE
- Author Notification: June 15, 2025, AoE
- Workshop Date: August 5, 2025 @ Edmonton, Canada
- Web page: prl-workshop.github.io
- Submission Deadline: May 30, 2025, AoE
- Author Notification: June 15, 2025, AoE
- Workshop Date: August 5, 2025 @ Edmonton, Canada
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Organizers: Shao-Hua Sun @shaohua0116, Levi Lelis
@levilelis, Xinyun Chen @xinyun_chen_, Shreyas Kapur
@shreyaskapur, Jiayuan Mao @maojiayuan, Ching-An Cheng @chinganc_rl, Anqi Li @AnqiLi24, Kuang-Huei Lee @kuanghueilee, and Leslie Kaelbling
@levilelis, Xinyun Chen @xinyun_chen_, Shreyas Kapur
@shreyaskapur, Jiayuan Mao @maojiayuan, Ching-An Cheng @chinganc_rl, Anqi Li @AnqiLi24, Kuang-Huei Lee @kuanghueilee, and Leslie Kaelbling
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